Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …

It is well documented that NLP models learn social biases, but little work has been done on
how these biases manifest in model outputs for applied tasks like question answering (QA) …

Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to
humans. For example, GPT-3 would mistakenly interpret" What word is similar to good?" to …

The rise of concern around Natural Language Processing (NLP) technologies containing
and perpetuating social biases has led to a rich and rapidly growing area of research …

The size of pretrained models is increasing, and so is their performance on a variety of NLP
tasks. However, as their memorization capacity grows, they might pick up more social …

Discriminatory social biases, including gender biases, have been found in Pre-trained
Language Models (PLMs). In Natural Language Inference (NLI), recent bias evaluation …

Common studies of gender bias in NLP focus either on extrinsic bias measured by model
performance on a downstream task or on intrinsic bias found in models' internal …

Large language models trained on a mixture of NLP tasks that are converted into a text-to-
text format using prompts, can generalize into novel forms of language and handle novel …

Much research has sought to evaluate the degree to which large language models reflect
social biases. We complement such work with an approach to elucidating the connections …

Discrete prompts have been used for fine-tuning Pre-trained Language Models for diverse
NLP tasks. In particular, automatic methods that generate discrete prompts from a small set …